Matrix interference, which can affect the accuracy of biosensor technologies
Research gap analysis derived from 4 biology papers in our local library.
The gap
One challenge is matrix interference, which can affect the accuracy of biosensor technologies. Another challenge is the lack of standardization in biosensor technologies, which can limit their clinical validation. The paper also identifies the need for more research on the integration of biosensors with artificial intelligence and digital systems as a significant challenge.
Evidence profile
Sourced from the stated research gap and future-work section and stated challenges of the source papers, classified as general, spanning 2 journals. Those papers have been cited 7 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 4 representative gaps
- Research Advances and Future Perspectives of Point-of-Care Detection Technologies and Biosensors for Mosquito-Borne Viruses (2026) · Biosensors · doi
few platforms currently integrate the advantages of point-of-care biosensors into a fully integrated and clinically validated sample-to-result workflow. limited comparisons and insufficient prospective clinical and field validation restrict the assessment of practical diagnostic utility. strong analytical performance alone should not be interpreted as evidence of clinical validity.
generalstated research gapKeywords: few platforms currently integrate advantages point-of-care biosensors fully - Beyond Detection Limits: Integrated Biosensors for Molecular Diagnostics, Longitudinal Monitoring, and Clinical Translation (2026) · Biosensors · doi
The lack of a unified translational framework for evaluating biosensor systems, leading to a focus on analytical sensitivity rather than clinically actionable information. The limited ability of biosensors to provide reliable, longitudinal, and clinically actionable information for molecular diagnosis and monitoring. The need for biosensors to evolve from isolated analytical detectors to integrated systems that can operate in complex biological matrices and near the patient.
generalstated research gapKeywords: lack unified translational framework evaluating biosensor systems leading - A RPA-CRISPR/Cas12a-Powered Catalytic Hairpin Assembly Fluorescence Biosensor for Duck Plague Virus Virulent Strain Detection (2026) · Biosensors · cited 3× · doi
Further studies are needed to evaluate the R-C-CHA biosensor's performance in complex samples and field settings, - The biosensor's detection limit and specificity should be further improved, - Other diagnostic methods should be compared to the R-C-CHA biosensor in detail, - The biosensor's potential for use in other applications, such as human disease diagnosis, should be explored
generalfuture-work sectionKeywords: further studies needed evaluate r-c-cha biosensor performance complex - Advances in biosensor technologies for the detection of antimicrobial resistance in Staphylococcus aureus (2026) · Frontiers in Cellular and Infection Microbiology · cited 4× · doi
One challenge is matrix interference, which can affect the accuracy of biosensor technologies. Another challenge is the lack of standardization in biosensor technologies, which can limit their clinical validation. The paper also identifies the need for more research on the integration of biosensors with artificial intelligence and digital systems as a significant challenge.
generalstated challengesevidence 5/5Keywords: one challenge matrix interference affect accuracy biosensor technologies
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